Andrew Sporle OS Forum 2014

The Maori preventable mortality project: an indigenous partnership initiative. A Sporle, C Kiro, N Pearce, A Lee, D Jansen IAOS 2014 1

The Context of Maori Health Development Persistent Disparities

Intra Ethnic Variation National Health Policies + Regional Funders Maori Service Providers – local – prevention focussed

URGENTLY Need more …. • Information that is • Accessible • MAORI focussed • Timely • LOCAL • Usable • Flexible • Rates not just counts • Future predictions 2

Persistent Disparities SRR Maori : NonMaori Rate Ratio 3.5 3.0 2.5 2.0 1.5

Source MoH 2010 1.0 0.5 Male

Female

0.0 1996

1997

1998

1999

2000

2001 Year

2002

2003

2004

2005

2006 3

Amenable Mortality Rate per 105

Intra-Ethnic Variation in Outcomes 1600 1400 1200 1000 Non-Maori Maori

800 600 400 200

0 1-2

3

4

5

6

Social Class

4

Indigenous Diversity by Geography Differences in: • Colonisation history • Years of contact • Social conditions • Language • Land retention • Governance • Resources

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Why do this? “to transform Maori mortality rates from being a measure of disparity to being a driver for action to improve Maori health at the local and national levels. “ 6

Needs to: • plan services • monitor outcomes Requires information: • local • recent • high quality

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Why Mortality? Key Public Health Measure: - Good quality ethnic data - Accessible by researchers - Regional data not published Preventability: 50-60% of difference in mortality due to PREVENTABLE causes Great EXEMPLAR for improving access and use 8

The Research Team Maori Provider Organisation

Researchers Andrew Sporle – social epidemiology Alan Lee – statistician (Auckland) Neil Pearce - epidemiology (LSHTM) Cindy Kiro – social policy (Victoria) Tony Blakely - epidemiologist (Otago Uni) Antony Raymont – health services

District Health Board

Advisory Groups Stakeholders – StatsNZ, Govt. ministries, local Maori organisations

Maori Researchers

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Research Questions How does preventable mortality vary by: • Time • Gender • Age group / cohort • Level of preventability • REGION Purpose: To identify the best places and ways to intervene 10

www.stat.auckland.ac.nz/~wild/iNZight/ © Departments of Statistics, The University of Auckland

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Data Visualiser Select: Population Region Gender Age Range

Males Māori

Non-Māori Ratio Pacific Non-Pacific Ratio Asian

Non-Asian Ratio

Amenable 320.5 296.7–345.7 113.6 109.1–118.3 2.82 248.2 216.8–282.9 128.2 123.5–133.0 1.94 63.8 51.5–78.0 138.3 133.3–143.4 0.46

Non-amenable 249.0 228.4–270.9 123.5 118.8–128.3 2.02 216.9 187.7–249.3 133.1 128.3–137.9 1.63 76.2 62.7–91.7 140.5 135.6–145.6 0.54

Females Amenable Non-amenable 189.6 197.9 172.2–208.3 180.1–217.1 63.1 88.3 59.8–66.5 84.4–92.2 3.00 2.24 160.0 139.5 136.1–187.0 117.5–164.5 72.4 97.2 69.0–76.0 93.3–101.2 2.21 1.44 43.8 44.2 34.1–55.3 34.2–56.3 78.5 103.0 74.8–82.2 98.9–107.2 0.56 0.43 Male, total New Zealand, 2006

Age standardised rates per 100,000

Standard Population

700 600 500 400

Comparison

300 200

Projection

100 0 Amenable

Non-amenable Maori

Total

Amenable

Non-amenable NonMaori

Total

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Improving Data Access Secure Data Archive

Datasets Epi Methods Protocols

Data visualiser

Regional Funders © Departments of Sociology and Statistics, The University of Auckland

Public Access Interface • Region • Gender • Age • Ref Population • Relative Risk • Confidence Ints. • Graphs and Tables

Maori Providers S13

Project Impact in 12 months Results: • Regional differences in data quality • Data visualiser pilot • Too early for other results Other Projects: - Mortality by migration and age cohort - Longitudinal mortality study

Student placements with providers: Teaching • Community • Providers • Students

Networks • Data users • Maori researchers 14

Maori Wellbeing Research Independent Maori Statutory Board Maori Development Plan • • • • •

Maori Values Key Directions Domains and Focus Areas Maori Outcomes Indicator wish list

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© Departments of Statistics, The University of Auckland

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The Future ? Improving responsive of official statistics leads to - Increased use - Improved perception - Improved quality - New ideas

If there is data – then make it available and useable 17